Individual differences in visual science: What can be learned and what is good experimental practice?

Mollon, J. D.; Bosten, Jenny; Peterzell, David H.; Webster, Michael A.

doi:10.1016/j.visres.2017.11.001

Cited by 109 publications

(109 citation statements)

References 148 publications

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“…Intrarater reliabilities were mostly significant but moderate, reflecting a nonnegligible within-participant variation that can be explained by the low number of repetitions per condition and by measurement errors. This may put an upper limit on the observed pairwise correlations, i.e., it may lead to underestimated correlations (Bosten et al, 2017;Mollon, Bosten, Peterzell, & Webster, 2017). To account for this, we computed disattenuated correlations.…”

Section: Discussionmentioning

confidence: 99%

Factors underlying visual illusions are illusion-specific but not feature-specific

et al. 2019

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Common factors are ubiquitous. For example, there is a common factor, g, for intelligence. In vision, there is much weaker evidence for such common factors. For example, visual illusion magnitudes correlate only weakly with each other. Here, we investigated whether illusions are hyper-specific as in perceptual learning. First, we tested 19 variants of the Ebbinghaus illusion that differed in color, shape, or texture. Correlations between the illusion magnitudes of the different variants were mostly significant. Second, we reanalyzed a dataset from a previous experiment where 10 illusions were tested under four conditions of luminance and found significant correlations between the different luminance conditions of each illusion. However, there were only very weak correlations between the 10 different illusions. Third, five visual illusions were tested with four orientations. Again, there were significant correlations between the four orientations of each illusion, but not across different illusions. The weak inter-illusion correlations suggest that there is no unique common mechanism for the tested illusions. We suggest that most illusions make up their own factor.

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Section: Discussionmentioning

confidence: 99%

Factors underlying visual illusions are illusion-specific but not feature-specific

et al. 2019

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“…One important aspect of these psychometric properties that Ramon et al rightly emphasize is whether a test can discriminate levels of performance across the full range of ability. But equally important are issues of test reliability (Mollon, Bosten, Petzerell, & Webster, 2017).…”

Section: Commentarymentioning

confidence: 99%

We need to talk about super‐recognizers Invited commentary on: Ramon, M., Bobak, A. K., & White, D. Super‐recognizers: From the lab to the world and back again. British Journal of Psychology.

Young

Noyes

2019

British J of Psychology

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“…During the exposure phase, participants viewed 60 complex visual patterns manipulated to contain features in a pseudo-randomised order (i.e. appeared in the same random order generated when programming the experiment) to minimise error variance across participants (Mollon, Bosten, Peterzell, & Webster, 2017). Exemplars were created by adding three features to the base pattern in opposing pairs (see Figure 1 and left panel of Figure 2).…”

Section: Methodsmentioning

confidence: 99%

Forensic feature-comparison expertise: Statistical learning facilitates visual comparison performance.

Growns¹,

Martire²

2020

Journal of Experimental Psychology: Applied

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Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing visual evidence samples. This paper examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relationship between distributional learning and visual comparison performance, and the impact of training about the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accurate training (informed novices; n = 32) or inaccurate training (misinformed novices; n = 32) in Experiment 1; and between forensic examiners (n = 26), informed novices (n = 29) and uninformed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and novices performed significantly above chance in a visual comparison task where distributional learning was required for high performance. However, informed novices outperformed all participants and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners' expertise is domain-specific and doesn't generalise to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance. Public Significance Statement:This study suggests that the ability to visually compare (or 'match') complex visual patterns can be improved by training individuals to use their knowledge of what features are rare or common in these patterns. This research has important practical implications for the performance of forensic feature-comparison examiners in the criminal justice system. Data, analysis code, supplementary material: https://osf.io/bh3yg/

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Individual differences in visual science: What can be learned and what is good experimental practice?

Cited by 109 publications

References 148 publications

Factors underlying visual illusions are illusion-specific but not feature-specific

Factors underlying visual illusions are illusion-specific but not feature-specific

We need to talk about super‐recognizers Invited commentary on: Ramon, M., Bobak, A. K., & White, D. Super‐recognizers: From the lab to the world and back again. British Journal of Psychology.

Forensic feature-comparison expertise: Statistical learning facilitates visual comparison performance.

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